Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations5000
Missing cells2827
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory898.6 KiB
Average record size in memory184.0 B

Variable types

Text2
Categorical7
Numeric12
Boolean2

Alerts

Internet_Access_at_Home is highly imbalanced (52.2%) Imbalance
Attendance (%) has 516 (10.3%) missing values Missing
Assignments_Avg has 517 (10.3%) missing values Missing
Parent_Education_Level has 1794 (35.9%) missing values Missing
Student_ID has unique values Unique
Email has unique values Unique

Reproduction

Analysis started2025-02-21 15:54:09.489413
Analysis finished2025-02-21 15:54:18.280231
Duration8.79 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Student_ID
Text

Unique 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:18.426001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters25000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st rowS1000
2nd rowS1001
3rd rowS1002
4th rowS1003
5th rowS1004
ValueCountFrequency (%)
s1008 1
 
< 0.1%
s5999 1
 
< 0.1%
s1000 1
 
< 0.1%
s1001 1
 
< 0.1%
s1002 1
 
< 0.1%
s1003 1
 
< 0.1%
s1004 1
 
< 0.1%
s1005 1
 
< 0.1%
s5984 1
 
< 0.1%
s5985 1
 
< 0.1%
Other values (4990) 4990
99.8%
2025-02-21T21:24:18.624047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 5000
20.0%
5 2500
10.0%
1 2500
10.0%
3 2500
10.0%
2 2500
10.0%
4 2500
10.0%
9 1500
 
6.0%
0 1500
 
6.0%
6 1500
 
6.0%
8 1500
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
80.0%
Uppercase Letter 5000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2500
12.5%
1 2500
12.5%
3 2500
12.5%
2 2500
12.5%
4 2500
12.5%
9 1500
7.5%
0 1500
7.5%
6 1500
7.5%
8 1500
7.5%
7 1500
7.5%
Uppercase Letter
ValueCountFrequency (%)
S 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
80.0%
Latin 5000
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2500
12.5%
1 2500
12.5%
3 2500
12.5%
2 2500
12.5%
4 2500
12.5%
9 1500
7.5%
0 1500
7.5%
6 1500
7.5%
8 1500
7.5%
7 1500
7.5%
Latin
ValueCountFrequency (%)
S 5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 5000
20.0%
5 2500
10.0%
1 2500
10.0%
3 2500
10.0%
2 2500
10.0%
4 2500
10.0%
9 1500
 
6.0%
0 1500
 
6.0%
6 1500
 
6.0%
8 1500
 
6.0%

First_Name
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Maria
657 
Ahmed
651 
Ali
644 
Emma
628 
Sara
612 
Other values (3)
1808 

Length

Max length5
Median length4
Mean length4.1328
Min length3

Characters and Unicode

Total characters20664
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOmar
2nd rowMaria
3rd rowAhmed
4th rowOmar
5th rowJohn

Common Values

ValueCountFrequency (%)
Maria 657
13.1%
Ahmed 651
13.0%
Ali 644
12.9%
Emma 628
12.6%
Sara 612
12.2%
John 608
12.2%
Omar 601
12.0%
Liam 599
12.0%

Length

2025-02-21T21:24:18.666469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T21:24:18.715703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
maria 657
13.1%
ahmed 651
13.0%
ali 644
12.9%
emma 628
12.6%
sara 612
12.2%
john 608
12.2%
omar 601
12.0%
liam 599
12.0%

Most occurring characters

ValueCountFrequency (%)
a 4366
21.1%
m 3107
15.0%
i 1900
9.2%
r 1870
9.0%
A 1295
 
6.3%
h 1259
 
6.1%
M 657
 
3.2%
e 651
 
3.2%
d 651
 
3.2%
l 644
 
3.1%
Other values (7) 4264
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15664
75.8%
Uppercase Letter 5000
 
24.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4366
27.9%
m 3107
19.8%
i 1900
12.1%
r 1870
11.9%
h 1259
 
8.0%
e 651
 
4.2%
d 651
 
4.2%
l 644
 
4.1%
o 608
 
3.9%
n 608
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
A 1295
25.9%
M 657
13.1%
E 628
12.6%
S 612
12.2%
J 608
12.2%
O 601
12.0%
L 599
12.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20664
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4366
21.1%
m 3107
15.0%
i 1900
9.2%
r 1870
9.0%
A 1295
 
6.3%
h 1259
 
6.1%
M 657
 
3.2%
e 651
 
3.2%
d 651
 
3.2%
l 644
 
3.1%
Other values (7) 4264
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4366
21.1%
m 3107
15.0%
i 1900
9.2%
r 1870
9.0%
A 1295
 
6.3%
h 1259
 
6.1%
M 657
 
3.2%
e 651
 
3.2%
d 651
 
3.2%
l 644
 
3.1%
Other values (7) 4264
20.6%

Last_Name
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Johnson
868 
Jones
850 
Davis
829 
Brown
825 
Smith
817 

Length

Max length8
Median length5
Mean length5.8338
Min length5

Characters and Unicode

Total characters29169
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWilliams
2nd rowBrown
3rd rowJones
4th rowWilliams
5th rowSmith

Common Values

ValueCountFrequency (%)
Johnson 868
17.4%
Jones 850
17.0%
Davis 829
16.6%
Brown 825
16.5%
Smith 817
16.3%
Williams 811
16.2%

Length

2025-02-21T21:24:18.788731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T21:24:18.836809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
johnson 868
17.4%
jones 850
17.0%
davis 829
16.6%
brown 825
16.5%
smith 817
16.3%
williams 811
16.2%

Most occurring characters

ValueCountFrequency (%)
o 3411
11.7%
n 3411
11.7%
s 3358
11.5%
i 3268
11.2%
J 1718
 
5.9%
h 1685
 
5.8%
a 1640
 
5.6%
m 1628
 
5.6%
l 1622
 
5.6%
e 850
 
2.9%
Other values (8) 6578
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24169
82.9%
Uppercase Letter 5000
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3411
14.1%
n 3411
14.1%
s 3358
13.9%
i 3268
13.5%
h 1685
7.0%
a 1640
6.8%
m 1628
6.7%
l 1622
6.7%
e 850
 
3.5%
v 829
 
3.4%
Other values (3) 2467
10.2%
Uppercase Letter
ValueCountFrequency (%)
J 1718
34.4%
D 829
16.6%
B 825
16.5%
S 817
16.3%
W 811
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 29169
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3411
11.7%
n 3411
11.7%
s 3358
11.5%
i 3268
11.2%
J 1718
 
5.9%
h 1685
 
5.8%
a 1640
 
5.6%
m 1628
 
5.6%
l 1622
 
5.6%
e 850
 
2.9%
Other values (8) 6578
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3411
11.7%
n 3411
11.7%
s 3358
11.5%
i 3268
11.2%
J 1718
 
5.9%
h 1685
 
5.8%
a 1640
 
5.6%
m 1628
 
5.6%
l 1622
 
5.6%
e 850
 
2.9%
Other values (8) 6578
22.6%

Email
Text

Unique 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:18.932569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length26
Mean length25.778
Min length23

Characters and Unicode

Total characters128890
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st rowstudent0@university.com
2nd rowstudent1@university.com
3rd rowstudent2@university.com
4th rowstudent3@university.com
5th rowstudent4@university.com
ValueCountFrequency (%)
student8@university.com 1
 
< 0.1%
student4999@university.com 1
 
< 0.1%
student0@university.com 1
 
< 0.1%
student1@university.com 1
 
< 0.1%
student2@university.com 1
 
< 0.1%
student3@university.com 1
 
< 0.1%
student4@university.com 1
 
< 0.1%
student5@university.com 1
 
< 0.1%
student4984@university.com 1
 
< 0.1%
student4985@university.com 1
 
< 0.1%
Other values (4990) 4990
99.8%
2025-02-21T21:24:19.075645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 15000
 
11.6%
s 10000
 
7.8%
u 10000
 
7.8%
e 10000
 
7.8%
n 10000
 
7.8%
i 10000
 
7.8%
d 5000
 
3.9%
v 5000
 
3.9%
@ 5000
 
3.9%
. 5000
 
3.9%
Other values (15) 43890
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100000
77.6%
Decimal Number 18890
 
14.7%
Other Punctuation 10000
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 15000
15.0%
s 10000
10.0%
u 10000
10.0%
e 10000
10.0%
n 10000
10.0%
i 10000
10.0%
d 5000
 
5.0%
v 5000
 
5.0%
c 5000
 
5.0%
r 5000
 
5.0%
Other values (3) 15000
15.0%
Decimal Number
ValueCountFrequency (%)
1 2500
13.2%
4 2500
13.2%
3 2500
13.2%
2 2500
13.2%
9 1500
7.9%
5 1500
7.9%
8 1500
7.9%
6 1500
7.9%
7 1500
7.9%
0 1390
7.4%
Other Punctuation
ValueCountFrequency (%)
@ 5000
50.0%
. 5000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100000
77.6%
Common 28890
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 15000
15.0%
s 10000
10.0%
u 10000
10.0%
e 10000
10.0%
n 10000
10.0%
i 10000
10.0%
d 5000
 
5.0%
v 5000
 
5.0%
c 5000
 
5.0%
r 5000
 
5.0%
Other values (3) 15000
15.0%
Common
ValueCountFrequency (%)
@ 5000
17.3%
. 5000
17.3%
1 2500
8.7%
4 2500
8.7%
3 2500
8.7%
2 2500
8.7%
9 1500
 
5.2%
5 1500
 
5.2%
8 1500
 
5.2%
6 1500
 
5.2%
Other values (2) 2890
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 15000
 
11.6%
s 10000
 
7.8%
u 10000
 
7.8%
e 10000
 
7.8%
n 10000
 
7.8%
i 10000
 
7.8%
d 5000
 
3.9%
v 5000
 
3.9%
@ 5000
 
3.9%
. 5000
 
3.9%
Other values (15) 43890
34.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Male
2551 
Female
2449 

Length

Max length6
Median length4
Mean length4.9796
Min length4

Characters and Unicode

Total characters24898
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 2551
51.0%
Female 2449
49.0%

Length

2025-02-21T21:24:19.141968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T21:24:19.178186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 2551
51.0%
female 2449
49.0%

Most occurring characters

ValueCountFrequency (%)
e 7449
29.9%
a 5000
20.1%
l 5000
20.1%
M 2551
 
10.2%
F 2449
 
9.8%
m 2449
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19898
79.9%
Uppercase Letter 5000
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7449
37.4%
a 5000
25.1%
l 5000
25.1%
m 2449
 
12.3%
Uppercase Letter
ValueCountFrequency (%)
M 2551
51.0%
F 2449
49.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24898
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7449
29.9%
a 5000
20.1%
l 5000
20.1%
M 2551
 
10.2%
F 2449
 
9.8%
m 2449
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7449
29.9%
a 5000
20.1%
l 5000
20.1%
M 2551
 
10.2%
F 2449
 
9.8%
m 2449
 
9.8%

Age
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0484
Minimum18
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:19.197806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q119
median21
Q323
95-th percentile24
Maximum24
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9897862
Coefficient of variation (CV)0.094533848
Kurtosis-1.2355855
Mean21.0484
Median Absolute Deviation (MAD)2
Skewness-0.037453183
Sum105242
Variance3.9592493
MonotonicityNot monotonic
2025-02-21T21:24:19.245078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21 753
15.1%
23 734
14.7%
22 732
14.6%
24 723
14.5%
19 705
14.1%
18 682
13.6%
20 671
13.4%
ValueCountFrequency (%)
18 682
13.6%
19 705
14.1%
20 671
13.4%
21 753
15.1%
22 732
14.6%
23 734
14.7%
24 723
14.5%
ValueCountFrequency (%)
24 723
14.5%
23 734
14.7%
22 732
14.6%
21 753
15.1%
20 671
13.4%
19 705
14.1%
18 682
13.6%

Department
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
CS
2022 
Engineering
1469 
Business
1006 
Mathematics
503 

Length

Max length11
Median length8
Mean length6.7568
Min length2

Characters and Unicode

Total characters33784
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngineering
2nd rowEngineering
3rd rowBusiness
4th rowMathematics
5th rowCS

Common Values

ValueCountFrequency (%)
CS 2022
40.4%
Engineering 1469
29.4%
Business 1006
20.1%
Mathematics 503
 
10.1%

Length

2025-02-21T21:24:19.292062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T21:24:19.329826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cs 2022
40.4%
engineering 1469
29.4%
business 1006
20.1%
mathematics 503
 
10.1%

Most occurring characters

ValueCountFrequency (%)
n 5413
16.0%
e 4447
13.2%
i 4447
13.2%
s 3521
10.4%
g 2938
8.7%
C 2022
 
6.0%
S 2022
 
6.0%
r 1469
 
4.3%
E 1469
 
4.3%
B 1006
 
3.0%
Other values (7) 5030
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26762
79.2%
Uppercase Letter 7022
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5413
20.2%
e 4447
16.6%
i 4447
16.6%
s 3521
13.2%
g 2938
11.0%
r 1469
 
5.5%
u 1006
 
3.8%
a 1006
 
3.8%
t 1006
 
3.8%
h 503
 
1.9%
Other values (2) 1006
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
C 2022
28.8%
S 2022
28.8%
E 1469
20.9%
B 1006
14.3%
M 503
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 33784
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5413
16.0%
e 4447
13.2%
i 4447
13.2%
s 3521
10.4%
g 2938
8.7%
C 2022
 
6.0%
S 2022
 
6.0%
r 1469
 
4.3%
E 1469
 
4.3%
B 1006
 
3.0%
Other values (7) 5030
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5413
16.0%
e 4447
13.2%
i 4447
13.2%
s 3521
10.4%
g 2938
8.7%
C 2022
 
6.0%
S 2022
 
6.0%
r 1469
 
4.3%
E 1469
 
4.3%
B 1006
 
3.0%
Other values (7) 5030
14.9%

Attendance (%)
Real number (ℝ)

Missing 

Distinct2980
Distinct (%)66.5%
Missing516
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean75.431409
Minimum50.01
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:19.372827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.01
5-th percentile52.6415
Q163.265
median75.725
Q387.4725
95-th percentile97.83
Maximum100
Range49.99
Interquartile range (IQR)24.2075

Descriptive statistics

Standard deviation14.372446
Coefficient of variation (CV)0.19053662
Kurtosis-1.1661556
Mean75.431409
Median Absolute Deviation (MAD)12.055
Skewness-0.038599792
Sum338234.44
Variance206.56719
MonotonicityNot monotonic
2025-02-21T21:24:19.428621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.9 6
 
0.1%
71.6 5
 
0.1%
86.72 5
 
0.1%
50.88 5
 
0.1%
96.09 5
 
0.1%
83.07 5
 
0.1%
75.29 5
 
0.1%
66.23 5
 
0.1%
92.88 4
 
0.1%
52.65 4
 
0.1%
Other values (2970) 4435
88.7%
(Missing) 516
 
10.3%
ValueCountFrequency (%)
50.01 1
< 0.1%
50.03 1
< 0.1%
50.04 1
< 0.1%
50.05 1
< 0.1%
50.06 2
< 0.1%
50.07 1
< 0.1%
50.1 1
< 0.1%
50.13 1
< 0.1%
50.14 1
< 0.1%
50.16 1
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
99.99 1
< 0.1%
99.96 2
< 0.1%
99.95 1
< 0.1%
99.94 1
< 0.1%
99.93 2
< 0.1%
99.91 1
< 0.1%
99.9 1
< 0.1%
99.89 2
< 0.1%
99.87 1
< 0.1%

Midterm_Score
Real number (ℝ)

Distinct3409
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.326844
Minimum40
Maximum99.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:19.496613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile43.29
Q155.4575
median70.51
Q384.97
95-th percentile97.121
Maximum99.98
Range59.98
Interquartile range (IQR)29.5125

Descriptive statistics

Standard deviation17.213209
Coefficient of variation (CV)0.24476014
Kurtosis-1.1778091
Mean70.326844
Median Absolute Deviation (MAD)14.77
Skewness-0.0220554
Sum351634.22
Variance296.29455
MonotonicityNot monotonic
2025-02-21T21:24:19.560172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.19 5
 
0.1%
93.56 5
 
0.1%
86.71 5
 
0.1%
87.83 5
 
0.1%
81.42 5
 
0.1%
81.59 5
 
0.1%
85 5
 
0.1%
50.51 5
 
0.1%
86.34 5
 
0.1%
44.03 4
 
0.1%
Other values (3399) 4951
99.0%
ValueCountFrequency (%)
40 1
< 0.1%
40.01 2
< 0.1%
40.02 1
< 0.1%
40.03 1
< 0.1%
40.06 1
< 0.1%
40.07 1
< 0.1%
40.09 2
< 0.1%
40.1 2
< 0.1%
40.12 1
< 0.1%
40.15 1
< 0.1%
ValueCountFrequency (%)
99.98 1
 
< 0.1%
99.97 1
 
< 0.1%
99.96 1
 
< 0.1%
99.93 1
 
< 0.1%
99.92 1
 
< 0.1%
99.91 1
 
< 0.1%
99.9 1
 
< 0.1%
99.89 1
 
< 0.1%
99.88 3
0.1%
99.85 3
0.1%

Final_Score
Real number (ℝ)

Distinct3371
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.640788
Minimum40
Maximum99.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:19.612459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42.978
Q154.6675
median69.735
Q384.5
95-th percentile96.79
Maximum99.98
Range59.98
Interquartile range (IQR)29.8325

Descriptive statistics

Standard deviation17.238744
Coefficient of variation (CV)0.24753804
Kurtosis-1.1999614
Mean69.640788
Median Absolute Deviation (MAD)14.945
Skewness0.018828204
Sum348203.94
Variance297.1743
MonotonicityNot monotonic
2025-02-21T21:24:19.674400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.47 6
 
0.1%
52.34 5
 
0.1%
54.02 5
 
0.1%
65.08 5
 
0.1%
44.49 5
 
0.1%
47.03 5
 
0.1%
59.86 5
 
0.1%
49.53 5
 
0.1%
46.4 5
 
0.1%
82.64 5
 
0.1%
Other values (3361) 4949
99.0%
ValueCountFrequency (%)
40 1
 
< 0.1%
40.01 3
0.1%
40.02 1
 
< 0.1%
40.07 1
 
< 0.1%
40.09 1
 
< 0.1%
40.12 2
< 0.1%
40.13 1
 
< 0.1%
40.18 1
 
< 0.1%
40.19 1
 
< 0.1%
40.2 1
 
< 0.1%
ValueCountFrequency (%)
99.98 3
0.1%
99.97 1
 
< 0.1%
99.96 1
 
< 0.1%
99.95 1
 
< 0.1%
99.94 1
 
< 0.1%
99.93 2
< 0.1%
99.91 1
 
< 0.1%
99.88 1
 
< 0.1%
99.86 2
< 0.1%
99.85 3
0.1%

Assignments_Avg
Real number (ℝ)

Missing 

Distinct2993
Distinct (%)66.8%
Missing517
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean74.798673
Minimum50
Maximum99.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:19.855305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52.451
Q162.09
median74.81
Q386.97
95-th percentile97.35
Maximum99.98
Range49.98
Interquartile range (IQR)24.88

Descriptive statistics

Standard deviation14.411799
Coefficient of variation (CV)0.19267453
Kurtosis-1.2007992
Mean74.798673
Median Absolute Deviation (MAD)12.46
Skewness0.016206722
Sum335322.45
Variance207.69996
MonotonicityNot monotonic
2025-02-21T21:24:19.918016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.69 6
 
0.1%
97.65 5
 
0.1%
52.16 5
 
0.1%
78.62 5
 
0.1%
95.96 5
 
0.1%
62.66 5
 
0.1%
69.33 5
 
0.1%
84.33 5
 
0.1%
92.42 5
 
0.1%
87.44 5
 
0.1%
Other values (2983) 4432
88.6%
(Missing) 517
 
10.3%
ValueCountFrequency (%)
50 2
< 0.1%
50.01 1
< 0.1%
50.02 1
< 0.1%
50.04 2
< 0.1%
50.06 1
< 0.1%
50.08 1
< 0.1%
50.09 2
< 0.1%
50.11 2
< 0.1%
50.13 2
< 0.1%
50.14 1
< 0.1%
ValueCountFrequency (%)
99.98 1
< 0.1%
99.96 1
< 0.1%
99.95 1
< 0.1%
99.93 2
< 0.1%
99.92 2
< 0.1%
99.91 1
< 0.1%
99.87 1
< 0.1%
99.85 1
< 0.1%
99.83 1
< 0.1%
99.81 1
< 0.1%

Quizzes_Avg
Real number (ℝ)

Distinct3173
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.910728
Minimum50.03
Maximum99.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:19.972685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.03
5-th percentile52.4175
Q162.49
median74.695
Q387.63
95-th percentile97.5705
Maximum99.96
Range49.93
Interquartile range (IQR)25.14

Descriptive statistics

Standard deviation14.504281
Coefficient of variation (CV)0.19362087
Kurtosis-1.2000987
Mean74.910728
Median Absolute Deviation (MAD)12.595
Skewness0.017547427
Sum374553.64
Variance210.37416
MonotonicityNot monotonic
2025-02-21T21:24:20.033710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.97 7
 
0.1%
91.53 6
 
0.1%
96.38 5
 
0.1%
52.25 5
 
0.1%
64.69 5
 
0.1%
94.06 5
 
0.1%
79.86 5
 
0.1%
58.92 5
 
0.1%
54.87 5
 
0.1%
73.79 5
 
0.1%
Other values (3163) 4947
98.9%
ValueCountFrequency (%)
50.03 3
0.1%
50.06 2
< 0.1%
50.07 2
< 0.1%
50.08 1
 
< 0.1%
50.09 1
 
< 0.1%
50.11 2
< 0.1%
50.13 2
< 0.1%
50.14 1
 
< 0.1%
50.16 1
 
< 0.1%
50.17 4
0.1%
ValueCountFrequency (%)
99.96 2
< 0.1%
99.95 1
< 0.1%
99.94 2
< 0.1%
99.93 1
< 0.1%
99.92 1
< 0.1%
99.91 1
< 0.1%
99.9 1
< 0.1%
99.87 1
< 0.1%
99.86 2
< 0.1%
99.85 1
< 0.1%

Participation_Score
Real number (ℝ)

Distinct997
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.980024
Minimum0
Maximum10
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:20.091846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.53
Q12.44
median4.955
Q37.5
95-th percentile9.49
Maximum10
Range10
Interquartile range (IQR)5.06

Descriptive statistics

Standard deviation2.890136
Coefficient of variation (CV)0.5803458
Kurtosis-1.2118751
Mean4.980024
Median Absolute Deviation (MAD)2.535
Skewness0.014938493
Sum24900.12
Variance8.3528862
MonotonicityNot monotonic
2025-02-21T21:24:20.151639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.68 13
 
0.3%
3.97 13
 
0.3%
0.41 12
 
0.2%
8.66 12
 
0.2%
8.23 11
 
0.2%
3.46 11
 
0.2%
8.6 11
 
0.2%
6.54 11
 
0.2%
9.96 11
 
0.2%
5.54 11
 
0.2%
Other values (987) 4884
97.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.01 3
 
0.1%
0.02 8
0.2%
0.03 3
 
0.1%
0.04 3
 
0.1%
0.05 5
0.1%
0.06 2
 
< 0.1%
0.07 4
0.1%
0.08 6
0.1%
0.09 4
0.1%
ValueCountFrequency (%)
10 3
 
0.1%
9.99 4
 
0.1%
9.98 10
0.2%
9.97 3
 
0.1%
9.96 11
0.2%
9.95 2
 
< 0.1%
9.94 9
0.2%
9.93 5
0.1%
9.92 6
0.1%
9.91 3
 
0.1%

Projects_Score
Real number (ℝ)

Distinct3141
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.92486
Minimum50.01
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:20.203192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.01
5-th percentile52.3195
Q162.32
median74.98
Q387.3675
95-th percentile97.23
Maximum100
Range49.99
Interquartile range (IQR)25.0475

Descriptive statistics

Standard deviation14.423415
Coefficient of variation (CV)0.19250506
Kurtosis-1.209599
Mean74.92486
Median Absolute Deviation (MAD)12.53
Skewness-0.0029773478
Sum374624.3
Variance208.03489
MonotonicityNot monotonic
2025-02-21T21:24:20.266412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.41 6
 
0.1%
81.23 6
 
0.1%
84.57 5
 
0.1%
84.13 5
 
0.1%
52.54 5
 
0.1%
57.64 5
 
0.1%
90.31 5
 
0.1%
53.68 5
 
0.1%
69.24 5
 
0.1%
90.16 5
 
0.1%
Other values (3131) 4948
99.0%
ValueCountFrequency (%)
50.01 1
 
< 0.1%
50.02 2
< 0.1%
50.03 4
0.1%
50.04 3
0.1%
50.05 2
< 0.1%
50.07 2
< 0.1%
50.08 1
 
< 0.1%
50.09 1
 
< 0.1%
50.12 2
< 0.1%
50.16 2
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
99.98 1
< 0.1%
99.97 1
< 0.1%
99.93 1
< 0.1%
99.92 1
< 0.1%
99.9 1
< 0.1%
99.89 2
< 0.1%
99.86 1
< 0.1%
99.85 1
< 0.1%
99.83 2
< 0.1%

Total_Score
Real number (ℝ)

Distinct3171
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.121804
Minimum50.02
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:20.342994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.02
5-th percentile52.4495
Q162.835
median75.395
Q387.6525
95-th percentile97.513
Maximum99.99
Range49.97
Interquartile range (IQR)24.8175

Descriptive statistics

Standard deviation14.399941
Coefficient of variation (CV)0.19168791
Kurtosis-1.1929169
Mean75.121804
Median Absolute Deviation (MAD)12.415
Skewness-0.022384696
Sum375609.02
Variance207.35831
MonotonicityNot monotonic
2025-02-21T21:24:20.403686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.03 7
 
0.1%
51.23 7
 
0.1%
70.3 7
 
0.1%
55.55 6
 
0.1%
50.49 6
 
0.1%
80.87 5
 
0.1%
89.86 5
 
0.1%
97.73 5
 
0.1%
94.33 5
 
0.1%
75.4 5
 
0.1%
Other values (3161) 4942
98.8%
ValueCountFrequency (%)
50.02 1
< 0.1%
50.03 2
< 0.1%
50.04 1
< 0.1%
50.05 1
< 0.1%
50.06 1
< 0.1%
50.07 2
< 0.1%
50.08 2
< 0.1%
50.09 1
< 0.1%
50.11 1
< 0.1%
50.12 1
< 0.1%
ValueCountFrequency (%)
99.99 3
0.1%
99.98 2
< 0.1%
99.96 1
 
< 0.1%
99.95 1
 
< 0.1%
99.94 1
 
< 0.1%
99.91 1
 
< 0.1%
99.9 1
 
< 0.1%
99.85 1
 
< 0.1%
99.84 1
 
< 0.1%
99.82 2
< 0.1%

Grade
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
A
1495 
B
978 
D
889 
F
844 
C
794 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowA
3rd rowD
4th rowA
5th rowF

Common Values

ValueCountFrequency (%)
A 1495
29.9%
B 978
19.6%
D 889
17.8%
F 844
16.9%
C 794
15.9%

Length

2025-02-21T21:24:20.460698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T21:24:20.493068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 1495
29.9%
b 978
19.6%
d 889
17.8%
f 844
16.9%
c 794
15.9%

Most occurring characters

ValueCountFrequency (%)
A 1495
29.9%
B 978
19.6%
D 889
17.8%
F 844
16.9%
C 794
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1495
29.9%
B 978
19.6%
D 889
17.8%
F 844
16.9%
C 794
15.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1495
29.9%
B 978
19.6%
D 889
17.8%
F 844
16.9%
C 794
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1495
29.9%
B 978
19.6%
D 889
17.8%
F 844
16.9%
C 794
15.9%

Study_Hours_per_Week
Real number (ℝ)

Distinct251
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.65886
Minimum5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:20.542270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.3
Q111.4
median17.5
Q324.1
95-th percentile28.9
Maximum30
Range25
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation7.2758643
Coefficient of variation (CV)0.41202344
Kurtosis-1.2220094
Mean17.65886
Median Absolute Deviation (MAD)6.3
Skewness0.00074335589
Sum88294.3
Variance52.938201
MonotonicityNot monotonic
2025-02-21T21:24:20.604158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6 32
 
0.6%
11.2 32
 
0.6%
29.3 30
 
0.6%
26.5 29
 
0.6%
16.9 29
 
0.6%
14.8 29
 
0.6%
24.1 29
 
0.6%
9.9 28
 
0.6%
12.9 28
 
0.6%
28.9 28
 
0.6%
Other values (241) 4706
94.1%
ValueCountFrequency (%)
5 11
0.2%
5.1 19
0.4%
5.2 16
0.3%
5.3 17
0.3%
5.4 23
0.5%
5.5 14
0.3%
5.6 16
0.3%
5.7 8
 
0.2%
5.8 27
0.5%
5.9 24
0.5%
ValueCountFrequency (%)
30 7
 
0.1%
29.9 19
0.4%
29.8 28
0.6%
29.7 23
0.5%
29.6 26
0.5%
29.5 24
0.5%
29.4 22
0.4%
29.3 30
0.6%
29.2 27
0.5%
29.1 23
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3493 
True
1507 
ValueCountFrequency (%)
False 3493
69.9%
True 1507
30.1%
2025-02-21T21:24:20.639266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Internet_Access_at_Home
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
4485 
False
515 
ValueCountFrequency (%)
True 4485
89.7%
False 515
 
10.3%
2025-02-21T21:24:20.662459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Parent_Education_Level
Categorical

Missing 

Distinct4
Distinct (%)0.1%
Missing1794
Missing (%)35.9%
Memory size39.2 KiB
PhD
820 
Bachelor's
810 
High School
796 
Master's
780 

Length

Max length11
Median length10
Mean length7.9713038
Min length3

Characters and Unicode

Total characters25556
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowMaster's
3rd rowHigh School
4th rowHigh School
5th rowPhD

Common Values

ValueCountFrequency (%)
PhD 820
16.4%
Bachelor's 810
16.2%
High School 796
15.9%
Master's 780
15.6%
(Missing) 1794
35.9%

Length

2025-02-21T21:24:20.702282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T21:24:20.743047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phd 820
20.5%
bachelor's 810
20.2%
high 796
19.9%
school 796
19.9%
master's 780
19.5%

Most occurring characters

ValueCountFrequency (%)
h 3222
12.6%
o 2402
 
9.4%
s 2370
 
9.3%
l 1606
 
6.3%
c 1606
 
6.3%
r 1590
 
6.2%
e 1590
 
6.2%
' 1590
 
6.2%
a 1590
 
6.2%
D 820
 
3.2%
Other values (9) 7170
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18348
71.8%
Uppercase Letter 4822
 
18.9%
Other Punctuation 1590
 
6.2%
Space Separator 796
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 3222
17.6%
o 2402
13.1%
s 2370
12.9%
l 1606
8.8%
c 1606
8.8%
r 1590
8.7%
e 1590
8.7%
a 1590
8.7%
i 796
 
4.3%
g 796
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
D 820
17.0%
P 820
17.0%
B 810
16.8%
H 796
16.5%
S 796
16.5%
M 780
16.2%
Other Punctuation
ValueCountFrequency (%)
' 1590
100.0%
Space Separator
ValueCountFrequency (%)
796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23170
90.7%
Common 2386
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 3222
13.9%
o 2402
10.4%
s 2370
10.2%
l 1606
 
6.9%
c 1606
 
6.9%
r 1590
 
6.9%
e 1590
 
6.9%
a 1590
 
6.9%
D 820
 
3.5%
P 820
 
3.5%
Other values (7) 5554
24.0%
Common
ValueCountFrequency (%)
' 1590
66.6%
796
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 3222
12.6%
o 2402
 
9.4%
s 2370
 
9.3%
l 1606
 
6.3%
c 1606
 
6.3%
r 1590
 
6.2%
e 1590
 
6.2%
' 1590
 
6.2%
a 1590
 
6.2%
D 820
 
3.2%
Other values (9) 7170
28.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Low
1983 
Medium
1973 
High
1044 

Length

Max length6
Median length4
Mean length4.3926
Min length3

Characters and Unicode

Total characters21963
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowLow
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
Low 1983
39.7%
Medium 1973
39.5%
High 1044
20.9%

Length

2025-02-21T21:24:20.789749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T21:24:20.822430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 1983
39.7%
medium 1973
39.5%
high 1044
20.9%

Most occurring characters

ValueCountFrequency (%)
i 3017
13.7%
L 1983
9.0%
w 1983
9.0%
o 1983
9.0%
M 1973
9.0%
e 1973
9.0%
d 1973
9.0%
u 1973
9.0%
m 1973
9.0%
H 1044
 
4.8%
Other values (2) 2088
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16963
77.2%
Uppercase Letter 5000
 
22.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3017
17.8%
w 1983
11.7%
o 1983
11.7%
e 1973
11.6%
d 1973
11.6%
u 1973
11.6%
m 1973
11.6%
g 1044
 
6.2%
h 1044
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
L 1983
39.7%
M 1973
39.5%
H 1044
20.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 21963
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3017
13.7%
L 1983
9.0%
w 1983
9.0%
o 1983
9.0%
M 1973
9.0%
e 1973
9.0%
d 1973
9.0%
u 1973
9.0%
m 1973
9.0%
H 1044
 
4.8%
Other values (2) 2088
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 3017
13.7%
L 1983
9.0%
w 1983
9.0%
o 1983
9.0%
M 1973
9.0%
e 1973
9.0%
d 1973
9.0%
u 1973
9.0%
m 1973
9.0%
H 1044
 
4.8%
Other values (2) 2088
9.5%

Stress_Level (1-10)
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4808
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:20.854220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8615501
Coefficient of variation (CV)0.52210446
Kurtosis-1.2241976
Mean5.4808
Median Absolute Deviation (MAD)2
Skewness0.014218697
Sum27404
Variance8.1884691
MonotonicityNot monotonic
2025-02-21T21:24:20.888980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 533
10.7%
8 524
10.5%
3 509
10.2%
2 504
10.1%
5 495
9.9%
1 489
9.8%
7 489
9.8%
10 488
9.8%
9 488
9.8%
6 481
9.6%
ValueCountFrequency (%)
1 489
9.8%
2 504
10.1%
3 509
10.2%
4 533
10.7%
5 495
9.9%
6 481
9.6%
7 489
9.8%
8 524
10.5%
9 488
9.8%
10 488
9.8%
ValueCountFrequency (%)
10 488
9.8%
9 488
9.8%
8 524
10.5%
7 489
9.8%
6 481
9.6%
5 495
9.9%
4 533
10.7%
3 509
10.2%
2 504
10.1%
1 489
9.8%

Sleep_Hours_per_Night
Real number (ℝ)

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.48814
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-02-21T21:24:20.936998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.2
Q15.2
median6.5
Q37.7
95-th percentile8.8
Maximum9
Range5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.4522834
Coefficient of variation (CV)0.22383664
Kurtosis-1.2035237
Mean6.48814
Median Absolute Deviation (MAD)1.3
Skewness-0.0033305739
Sum32440.7
Variance2.1091272
MonotonicityNot monotonic
2025-02-21T21:24:20.992437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 119
 
2.4%
6.8 116
 
2.3%
4.2 116
 
2.3%
7.7 115
 
2.3%
4.3 113
 
2.3%
7.9 113
 
2.3%
6.9 112
 
2.2%
8.9 112
 
2.2%
4.7 112
 
2.2%
4.9 110
 
2.2%
Other values (41) 3862
77.2%
ValueCountFrequency (%)
4 49
1.0%
4.1 104
2.1%
4.2 116
2.3%
4.3 113
2.3%
4.4 104
2.1%
4.5 91
1.8%
4.6 100
2.0%
4.7 112
2.2%
4.8 99
2.0%
4.9 110
2.2%
ValueCountFrequency (%)
9 49
1.0%
8.9 112
2.2%
8.8 90
1.8%
8.7 98
2.0%
8.6 100
2.0%
8.5 104
2.1%
8.4 92
1.8%
8.3 95
1.9%
8.2 99
2.0%
8.1 91
1.8%

Interactions

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2025-02-21T21:24:12.564016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:13.162463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-21T21:24:14.412385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-21T21:24:15.568234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-21T21:24:16.567061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:17.240563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:17.859203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:11.247555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:11.851595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:12.459857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:13.066090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:13.648162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:14.312381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:14.894994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:15.486562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:16.048766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:16.616602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:17.283124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:17.907862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:11.294363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:11.900161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:12.508867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:13.115947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:13.696923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:14.361057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:14.946345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:15.533025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:16.081511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:16.660113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T21:24:17.332097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-21T21:24:21.048728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAssignments_AvgAttendance (%)DepartmentExtracurricular_ActivitiesFamily_Income_LevelFinal_ScoreFirst_NameGenderGradeInternet_Access_at_HomeLast_NameMidterm_ScoreParent_Education_LevelParticipation_ScoreProjects_ScoreQuizzes_AvgSleep_Hours_per_NightStress_Level (1-10)Study_Hours_per_WeekTotal_Score
Age1.000-0.0330.0100.0160.0320.012-0.0160.0000.0290.0000.0000.0000.0060.031-0.0140.0030.007-0.005-0.0050.0020.017
Assignments_Avg-0.0331.000-0.0380.0000.0280.0350.0100.0000.0340.0000.0160.000-0.0070.0000.0140.0070.0070.0190.0110.0240.001
Attendance (%)0.010-0.0381.0000.0170.0000.000-0.0260.0000.0000.3740.0000.026-0.0080.000-0.030-0.013-0.029-0.0360.0120.016-0.020
Department0.0160.0000.0171.0000.0000.0230.0290.0250.0000.0110.0000.0280.0000.0000.0000.0000.0270.0310.0000.0190.000
Extracurricular_Activities0.0320.0280.0000.0001.0000.0060.0000.0080.0000.0260.0000.0000.0000.0000.0310.0000.0000.0230.0390.0000.000
Family_Income_Level0.0120.0350.0000.0230.0061.0000.0190.0180.0260.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0300.015
Final_Score-0.0160.010-0.0260.0290.0000.0191.0000.0170.0000.0110.0310.0000.0010.0000.014-0.0030.003-0.014-0.0000.0090.007
First_Name0.0000.0000.0000.0250.0080.0180.0171.0000.0000.0130.0000.0000.0140.0000.0000.0150.0000.0220.0190.0100.030
Gender0.0290.0340.0000.0000.0000.0260.0000.0001.0000.0000.0000.0000.0260.0000.0000.0330.0120.0000.0000.0000.000
Grade0.0000.0000.3740.0110.0260.0000.0110.0130.0001.0000.0000.0130.0260.0000.0100.0000.0000.0300.0000.0190.033
Internet_Access_at_Home0.0000.0160.0000.0000.0000.0000.0310.0000.0000.0001.0000.0080.0000.0340.0000.0000.0000.0000.0000.0000.000
Last_Name0.0000.0000.0260.0280.0000.0000.0000.0000.0000.0130.0081.0000.0220.0000.0100.0000.0000.0090.0000.0000.000
Midterm_Score0.006-0.007-0.0080.0000.0000.0000.0010.0140.0260.0260.0000.0221.0000.034-0.0010.014-0.011-0.0040.0200.002-0.001
Parent_Education_Level0.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0341.0000.0090.0000.0000.0000.0000.0000.000
Participation_Score-0.0140.014-0.0300.0000.0310.0000.0140.0000.0000.0100.0000.010-0.0010.0091.000-0.026-0.006-0.006-0.006-0.005-0.034
Projects_Score0.0030.007-0.0130.0000.0000.000-0.0030.0150.0330.0000.0000.0000.0140.000-0.0261.0000.005-0.004-0.0150.004-0.018
Quizzes_Avg0.0070.007-0.0290.0270.0000.0000.0030.0000.0120.0000.0000.000-0.0110.000-0.0060.0051.0000.0020.0010.0200.014
Sleep_Hours_per_Night-0.0050.019-0.0360.0310.0230.016-0.0140.0220.0000.0300.0000.009-0.0040.000-0.006-0.0040.0021.0000.008-0.0040.002
Stress_Level (1-10)-0.0050.0110.0120.0000.0390.000-0.0000.0190.0000.0000.0000.0000.0200.000-0.006-0.0150.0010.0081.0000.0040.004
Study_Hours_per_Week0.0020.0240.0160.0190.0000.0300.0090.0100.0000.0190.0000.0000.0020.000-0.0050.0040.020-0.0040.0041.000-0.013
Total_Score0.0170.001-0.0200.0000.0000.0150.0070.0300.0000.0330.0000.000-0.0010.000-0.034-0.0180.0140.0020.004-0.0131.000

Missing values

2025-02-21T21:24:17.998408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-21T21:24:18.108561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-21T21:24:18.239022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Student_IDFirst_NameLast_NameEmailGenderAgeDepartmentAttendance (%)Midterm_ScoreFinal_ScoreAssignments_AvgQuizzes_AvgParticipation_ScoreProjects_ScoreTotal_ScoreGradeStudy_Hours_per_WeekExtracurricular_ActivitiesInternet_Access_at_HomeParent_Education_LevelFamily_Income_LevelStress_Level (1-10)Sleep_Hours_per_Night
0S1000OmarWilliamsstudent0@university.comFemale22Engineering52.2955.0357.8284.2274.063.9985.9056.09F6.2NoYesHigh SchoolMedium54.7
1S1001MariaBrownstudent1@university.comMale18Engineering97.2797.2345.80NaN94.248.3255.6550.64A19.0NoYesNaNMedium49.0
2S1002AhmedJonesstudent2@university.comMale24Business57.1967.0593.6867.7085.705.0573.7970.30D20.7NoYesMaster'sLow66.2
3S1003OmarWilliamsstudent3@university.comFemale24Mathematics95.1547.7980.6366.0693.516.5492.1261.63A24.8YesYesHigh SchoolHigh36.7
4S1004JohnSmithstudent4@university.comFemale23CS54.1846.5978.8996.8583.705.9768.4266.13F15.4YesYesHigh SchoolHigh27.1
5S1005LiamBrownstudent5@university.comMale21EngineeringNaN78.8543.5371.4052.206.3867.2962.08B8.5YesYesPhDHigh15.0
6S1006AhmedJonesstudent6@university.comMale24Business57.6066.2689.0784.5298.402.3093.6583.21F21.3NoYesNaNLow56.4
7S1007AhmedSmithstudent7@university.comMale19Engineering51.9145.6773.9680.1295.903.7393.2481.93F27.3YesNoNaNMedium44.3
8S1008OmarSmithstudent8@university.comFemale21CS85.9784.4290.8757.0556.330.5194.0195.62A8.0NoNoBachelor'sLow98.8
9S1009SaraSmithstudent9@university.comFemale22Engineering64.0187.9698.4796.9855.635.8878.6084.99A9.6NoYesNaNMedium106.4
Student_IDFirst_NameLast_NameEmailGenderAgeDepartmentAttendance (%)Midterm_ScoreFinal_ScoreAssignments_AvgQuizzes_AvgParticipation_ScoreProjects_ScoreTotal_ScoreGradeStudy_Hours_per_WeekExtracurricular_ActivitiesInternet_Access_at_HomeParent_Education_LevelFamily_Income_LevelStress_Level (1-10)Sleep_Hours_per_Night
4990S5990AliJohnsonstudent4990@university.comMale24CS80.5387.8343.7069.2969.461.8465.1681.18F15.2NoYesNaNLow77.1
4991S5991JohnWilliamsstudent4991@university.comFemale20Engineering55.5459.6863.09NaN56.922.3281.3089.55D18.8YesYesNaNLow56.8
4992S5992SaraJohnsonstudent4992@university.comMale18CSNaN61.4464.59NaN50.113.5380.0862.51A23.3YesNoHigh SchoolMedium16.2
4993S5993AliJohnsonstudent4993@university.comFemale22Business99.2160.1659.1859.7463.012.7069.5685.86A16.9NoYesNaNLow27.8
4994S5994JohnJohnsonstudent4994@university.comFemale19MathematicsNaN69.9255.4875.2654.368.0364.3655.88F25.5NoYesHigh SchoolLow24.1
4995S5995AhmedJonesstudent4995@university.comMale19BusinessNaN82.1560.3380.0999.325.0058.4285.21D25.5NoYesHigh SchoolLow108.3
4996S5996EmmaBrownstudent4996@university.comMale19Business65.1186.3149.80NaN88.082.7960.8795.96C5.0NoYesNaNMedium44.0
4997S5997JohnBrownstudent4997@university.comFemale24CS87.5463.5564.2194.2850.193.1382.6554.25A24.8YesNoHigh SchoolMedium46.3
4998S5998SaraDavisstudent4998@university.comMale23CS92.5679.7994.2881.2061.180.4094.2955.84A16.1YesYesBachelor'sLow18.4
4999S5999MariaBrownstudent4999@university.comFemale21Engineering83.9283.2453.4751.7683.510.4969.2577.86F29.2NoYesPhDLow26.1